{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "5bf1de44-4047-46cf-a04c-dbf910d9e179",
            "metadata": {},
            "source": [
                "# Router Retriever\n",
                "In this guide, we define a custom router retriever that selects one or more candidate retrievers in order to execute a given query.\n",
                "\n",
                "The router (`BaseSelector`) module uses the LLM to dynamically make decisions on which underlying retrieval tools to use. This can be helpful to select one out of a diverse range of data sources. This can also be helpful to aggregate retrieval results across a variety of data sources (if a multi-selector module is used).\n",
                "\n",
                "This notebook is very similar to the RouterQueryEngine notebook."
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6e73fead-ec2c-4346-bd08-e183c13c7e29",
            "metadata": {},
            "source": [
                "### Setup"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "a2d59778-4cda-47b5-8cd0-b80fee91d1e4",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# NOTE: This is ONLY necessary in jupyter notebook.\n",
                "# Details: Jupyter runs an event-loop behind the scenes.\n",
                "#          This results in nested event-loops when we start an event-loop to make async queries.\n",
                "#          This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
                "import nest_asyncio\n",
                "\n",
                "nest_asyncio.apply()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "c628448c-573c-4eeb-a7e1-707fe8cc575c",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
                        "NumExpr defaulting to 8 threads.\n"
                    ]
                }
            ],
            "source": [
                "import logging\n",
                "import sys\n",
                "\n",
                "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
                "logging.getLogger().handlers = []\n",
                "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
                "\n",
                "from llama_index import (\n",
                "    VectorStoreIndex,\n",
                "    SummaryIndex,\n",
                "    SimpleDirectoryReader,\n",
                "    ServiceContext,\n",
                "    StorageContext,\n",
                "    SimpleKeywordTableIndex,\n",
                ")\n",
                "from llama_index.llms import OpenAI"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "787174ed-10ce-47d7-82fd-9ca7f891eea7",
            "metadata": {},
            "source": [
                "### Load Data\n",
                "\n",
                "We first show how to convert a Document into a set of Nodes, and insert into a DocumentStore."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "1fc1b8ac-bf55-4d60-841c-61698663322f",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# load documents\n",
                "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "7081194a-ede7-478e-bff2-23e89e23ef16",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# initialize service context (set chunk size)\n",
                "llm = OpenAI(model=\"gpt-4\")\n",
                "service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm)\n",
                "nodes = service_context.node_parser.get_nodes_from_documents(documents)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "8f61bca2-c3b4-4ef0-a8f1-367933aa6d05",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# initialize storage context (by default it's in-memory)\n",
                "storage_context = StorageContext.from_defaults()\n",
                "storage_context.docstore.add_documents(nodes)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "c8f5c44f-11d2-47a2-a566-c6dc0fd5a1c3",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "# define\n",
                "summary_index = SummaryIndex(nodes, storage_context=storage_context)\n",
                "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)\n",
                "keyword_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "0d6162df-9da7-4aad-a2ca-eb318f67daec",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "list_retriever = summary_index.as_retriever()\n",
                "vector_retriever = vector_index.as_retriever()\n",
                "keyword_retriever = keyword_index.as_retriever()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "ee3f7c3b-69b4-48d5-bf22-ac51a4e3179f",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from llama_index.tools import RetrieverTool\n",
                "\n",
                "list_tool = RetrieverTool.from_defaults(\n",
                "    retriever=list_retriever,\n",
                "    description=\"Will retrieve all context from Paul Graham's essay on What I Worked On. Don't use if the question only requires more specific context.\",\n",
                ")\n",
                "vector_tool = RetrieverTool.from_defaults(\n",
                "    retriever=vector_retriever,\n",
                "    description=\"Useful for retrieving specific context from Paul Graham essay on What I Worked On.\",\n",
                ")\n",
                "keyword_tool = RetrieverTool.from_defaults(\n",
                "    retriever=keyword_retriever,\n",
                "    description=\"Useful for retrieving specific context from Paul Graham essay on What I Worked On (using entities mentioned in query)\",\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "0bba2d68-13f9-4519-87ec-40511da7abdd",
            "metadata": {},
            "source": [
                "### Define Selector Module for Routing\n",
                "\n",
                "There are several selectors available, each with some distinct attributes.\n",
                "\n",
                "The LLM selectors use the LLM to output a JSON that is parsed, and the corresponding indexes are queried.\n",
                "\n",
                "The Pydantic selectors (currently only supported by `gpt-4-0613` and `gpt-3.5-turbo-0613` (the default)) use the OpenAI Function Call API to produce pydantic selection objects, rather than parsing raw JSON.\n",
                "\n",
                "Here we use PydanticSingleSelector/PydanticMultiSelector but you can use the LLM-equivalents as well. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "6cb64a55-05b7-4565-949b-025b8d19c375",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "from llama_index.selectors.llm_selectors import LLMSingleSelector, LLMMultiSelector\n",
                "from llama_index.selectors.pydantic_selectors import (\n",
                "    PydanticMultiSelector,\n",
                "    PydanticSingleSelector,\n",
                ")\n",
                "from llama_index.retrievers import RouterRetriever\n",
                "from llama_index.response.notebook_utils import display_source_node"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "3513ca57-bef9-47d3-aa17-3cf72a6eb318",
            "metadata": {},
            "source": [
                "#### PydanticSingleSelector"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "8ecb1c95-0096-4036-ad32-2337d844bf68",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "retriever = RouterRetriever(\n",
                "    selector=PydanticSingleSelector.from_defaults(llm=llm),\n",
                "    retriever_tools=[\n",
                "        list_tool,\n",
                "        vector_tool,\n",
                "    ],\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "7b8c4c12-1a30-425e-8312-04be050b2101",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Selecting retriever 0: This choice is most relevant as it mentions retrieving all context from the essay, which could include information about the author's life..\n"
                    ]
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 7d07d325-489e-4157-a745-270e2066a643<br>**Similarity:** None<br>**Text:** What I Worked On\n",
                            "\n",
                            "February 2021\n",
                            "\n",
                            "Before college the two main things I worked on, outside of schoo...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 01f0900b-db83-450b-a088-0473f16882d7<br>**Similarity:** None<br>**Text:** showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** b2549a68-5fef-4179-b027-620ebfa6e346<br>**Similarity:** None<br>**Text:** Science is an uneasy alliance between two halves, theory and systems. The theory people prove thi...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 4f1e9f0d-9bc6-4169-b3b6-4f169bbfa391<br>**Similarity:** None<br>**Text:** been explored. But all I wanted was to get out of grad school, and my rapidly written dissertatio...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** e20c99f9-5e80-4c92-8cc0-03d2a527131e<br>**Similarity:** None<br>**Text:** stop there, of course, or you get merely photographic accuracy, and what makes a still life inter...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** dbdf341a-f340-49f9-961f-16b9a51eea2d<br>**Similarity:** None<br>**Text:** that big, bureaucratic customers are a dangerous source of money, and that there's not much overl...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** ed341d3a-9dda-49c1-8611-0ab40d04f08a<br>**Similarity:** None<br>**Text:** about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking wor...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** d69e02d3-2732-4567-a360-893c14ae157b<br>**Similarity:** None<br>**Text:** a web app, is common now, but at the time it wasn't clear that it was even possible. To find out,...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** df9e00a5-e795-40a1-9a6b-8184d1b1e7c0<br>**Similarity:** None<br>**Text:** have to integrate with any other software except Robert's and Trevor's, so it was quite fun to wo...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 38f2699b-0878-499b-90ee-821cb77e387b<br>**Similarity:** None<br>**Text:** all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** be04d6a9-1fc7-4209-9df2-9c17a453699a<br>**Similarity:** None<br>**Text:** for a second still life, painted from the same objects (which hopefully hadn't rotted yet).\n",
                            "\n",
                            "Mean...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 42344911-8a7c-4e9b-81a8-0fcf40ab7690<br>**Similarity:** None<br>**Text:** which I'd created years before using Viaweb but had never used for anything. In one day it got 30...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 9ec3df49-abf9-47f4-b0c2-16687882742a<br>**Similarity:** None<br>**Text:** I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** d0cf6975-5261-4fb2-aae3-f3230090fb64<br>**Similarity:** None<br>**Text:** of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" B...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 607d0480-7eee-4fb4-965d-3cb585fda62c<br>**Similarity:** None<br>**Text:** to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get t...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 730a49c9-55f7-4416-ab91-1d0c96e704c8<br>**Similarity:** None<br>**Text:** So this set me thinking. It was true that on my current trajectory, YC would be the last thing I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** edbe8c67-e373-42bf-af98-276b559cc08b<br>**Similarity:** None<br>**Text:** operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an an...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 175a4375-35ec-45a0-a90c-15611505096b<br>**Similarity:** None<br>**Text:** Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy'...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 0cb367f9-0aac-422b-9243-0eaa7be15090<br>**Similarity:** None<br>**Text:** must tell readers things they don't already know, and some people dislike being told such things....<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 67afd4f1-9fa1-4e76-87ac-23b115823e6c<br>**Similarity:** None<br>**Text:** 1960 paper.\n",
                            "\n",
                            "But if so there's no reason to suppose that this is the limit of the language that m...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# will retrieve all context from the author's life\n",
                "nodes = retriever.retrieve(\n",
                "    \"Can you give me all the context regarding the author's life?\"\n",
                ")\n",
                "for node in nodes:\n",
                "    display_source_node(node)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "2749c34e-97c0-4bd5-8358-377a94b8b2d8",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Selecting retriever 1: The question asks for a specific detail from Paul Graham's essay on 'What I Worked On'. Therefore, the second choice, which is useful for retrieving specific context, is the most relevant..\n"
                    ]
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 22d20835-7de6-4cf7-92de-2bee339f3157<br>**Similarity:** 0.8017176790752668<br>**Text:** that big, bureaucratic customers are a dangerous source of money, and that there's not much overl...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** bf818c58-5d5b-4458-acbc-d87cc67a36ca<br>**Similarity:** 0.7935885352785799<br>**Text:** So this set me thinking. It was true that on my current trajectory, YC would be the last thing I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "nodes = retriever.retrieve(\"What did Paul Graham do after RISD?\")\n",
                "for node in nodes:\n",
                "    display_source_node(node)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "fae962a0-55c3-42e4-8f90-8332499952b5",
            "metadata": {},
            "source": [
                "#### PydanticMultiSelector"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "d93cd132-fa4d-431f-9b02-0fc7482f097e",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "retriever = RouterRetriever(\n",
                "    selector=PydanticMultiSelector.from_defaults(llm=llm),\n",
                "    retriever_tools=[list_tool, vector_tool, keyword_tool],\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "id": "62b877dc-50d9-4841-9747-d902a60b767f",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Selecting retriever 1: This choice is relevant as it allows for retrieving specific context from the essay, which is needed to answer the question about notable events at Interleaf and YC..\n",
                        "Selecting retriever 2: This choice is also relevant as it allows for retrieving specific context using entities mentioned in the query, which in this case are 'Interleaf' and 'YC'..\n",
                        "> Starting query: What were noteable events from the authors time at Interleaf and YC?\n",
                        "query keywords: ['interleaf', 'events', 'noteable', 'yc']\n",
                        "> Extracted keywords: ['interleaf', 'yc']\n"
                    ]
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** fbdd25ed-1ecb-4528-88da-34f581c30782<br>**Similarity:** None<br>**Text:** So this set me thinking. It was true that on my current trajectory, YC would be the last thing I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 4ce91b17-131f-4155-b7b5-8917cdc612b1<br>**Similarity:** None<br>**Text:** to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get t...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 9fe6c152-28d4-4006-8a1a-43bb72655438<br>**Similarity:** None<br>**Text:** stop there, of course, or you get merely photographic accuracy, and what makes a still life inter...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** d11cd2e2-1dd2-4c3b-863f-246fe3856f49<br>**Similarity:** None<br>**Text:** of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" B...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 2bfbab04-cb71-4641-9bd9-52c75b3a9250<br>**Similarity:** None<br>**Text:** must tell readers things they don't already know, and some people dislike being told such things....<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "nodes = retriever.retrieve(\n",
                "    \"What were noteable events from the authors time at Interleaf and YC?\"\n",
                ")\n",
                "for node in nodes:\n",
                "    display_source_node(node)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "af51424b-d0b1-4c07-acf3-53e398a7d783",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Selecting retriever 1: This choice is relevant as it allows for retrieving specific context from the essay, which is needed to answer the question about notable events at Interleaf and YC..\n",
                        "Selecting retriever 2: This choice is also relevant as it allows for retrieving specific context using entities mentioned in the query, which in this case are 'Interleaf' and 'YC'..\n",
                        "> Starting query: What were noteable events from the authors time at Interleaf and YC?\n",
                        "query keywords: ['interleaf', 'yc', 'events', 'noteable']\n",
                        "> Extracted keywords: ['interleaf', 'yc']\n"
                    ]
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 49882a2c-bb95-4ff3-9df1-2a40ddaea408<br>**Similarity:** None<br>**Text:** So this set me thinking. It was true that on my current trajectory, YC would be the last thing I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** d11aced1-e630-4109-8ec8-194e975b9851<br>**Similarity:** None<br>**Text:** to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get t...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 8aa6cc91-8e9c-4470-b6d5-4360ed13fefd<br>**Similarity:** None<br>**Text:** stop there, of course, or you get merely photographic accuracy, and what makes a still life inter...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** e37465de-c79a-4714-a402-fbd5f52800a2<br>**Similarity:** None<br>**Text:** must tell readers things they don't already know, and some people dislike being told such things....<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** e0ac7fb6-84fc-4763-bca6-b68f300ec7b7<br>**Similarity:** None<br>**Text:** of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" B...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "nodes = retriever.retrieve(\n",
                "    \"What were noteable events from the authors time at Interleaf and YC?\"\n",
                ")\n",
                "for node in nodes:\n",
                "    display_source_node(node)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "26e1398d-cc34-44d3-a8a1-fc521e3ba009",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Selecting retriever 1: This choice is relevant as it allows for retrieving specific context from the essay, which is needed to answer the question about notable events at Interleaf and YC..\n",
                        "Selecting retriever 2: This choice is also relevant as it allows for retrieving specific context using entities mentioned in the query, which in this case are 'Interleaf' and 'YC'..\n",
                        "> Starting query: What were noteable events from the authors time at Interleaf and YC?\n",
                        "query keywords: ['events', 'interleaf', 'yc', 'noteable']\n",
                        "> Extracted keywords: ['interleaf', 'yc']\n",
                        "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=25 request_id=95c73e9360e6473daab85cde93ca4c42 response_code=200\n"
                    ]
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 76d76348-52fb-49e6-95b8-2f7a3900fa1a<br>**Similarity:** None<br>**Text:** So this set me thinking. It was true that on my current trajectory, YC would be the last thing I ...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 61e1908a-79d2-426b-840e-926df469ac49<br>**Similarity:** None<br>**Text:** to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get t...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** cac03004-5c02-4145-8e92-c320b1803847<br>**Similarity:** None<br>**Text:** stop there, of course, or you get merely photographic accuracy, and what makes a still life inter...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** f0d55e5e-5349-4243-ab01-d9dd7b12cd0a<br>**Similarity:** None<br>**Text:** of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" B...<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "**Node ID:** 1516923c-0dee-4af2-b042-3e1f38de7e86<br>**Similarity:** None<br>**Text:** must tell readers things they don't already know, and some people dislike being told such things....<br>"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "nodes = await retriever.aretrieve(\n",
                "    \"What were noteable events from the authors time at Interleaf and YC?\"\n",
                ")\n",
                "for node in nodes:\n",
                "    display_source_node(node)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "719bdc86-7015-4350-af63-8699a1949394",
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "llama_index_v2",
            "language": "python",
            "name": "llama_index_v2"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.10.10"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}
